{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T12:35:14.918127Z",
     "start_time": "2025-02-26T12:35:14.271499Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from matplotlib import  pyplot as plt"
   ],
   "id": "7968cefd65209b06",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T12:35:31.436987Z",
     "start_time": "2025-02-26T12:35:31.431897Z"
    }
   },
   "cell_type": "code",
   "source": [
    "us_file_path = \"./youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = \"./youtube_video_data/GB_video_data_numbers.csv\""
   ],
   "id": "9016226c36c7587b",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T12:35:57.975995Z",
     "start_time": "2025-02-26T12:35:57.774569Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 打印直方图\n",
    "t_us = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\")\n",
    "print(t_us.shape)\n",
    "\n",
    "# 取评论的数据\n",
    "t_us_comments = t_us[:,-1]\n",
    "\n",
    "# 怎么知道分多少，打印最大和最小值\n",
    "print(t_us_comments.max(),t_us_comments.min())\n",
    "#组距为10000\n",
    "d = 10000\n",
    "# 计算组数\n",
    "bin_nums = (t_us_comments.max()-t_us_comments.min())//d\n",
    "\n",
    "# 绘制直方图 hist\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.hist(t_us_comments,bin_nums)\n",
    "\n",
    "plt.show()"
   ],
   "id": "37a339ed76bc7f24",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1688, 4)\n",
      "582624 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T12:55:29.379067Z",
     "start_time": "2025-02-26T12:55:29.218879Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 过滤掉评论数小于等于5000的数据\n",
    "#选择比5000小的数据\n",
    "t_us_comments = t_us_comments[t_us_comments<=5000]\n",
    "print(t_us_comments.shape) #剩余多少样本\n",
    "print(t_us_comments.max(),t_us_comments.min())\n",
    "# 组距为50\n",
    "d = 50\n",
    "# 计算组数\n",
    "bin_nums = (t_us_comments.max()-t_us_comments.min())//d\n",
    "\n",
    "#绘图\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "\n",
    "plt.hist(t_us_comments,bin_nums)\n",
    "\n",
    "\n",
    "plt.show()"
   ],
   "id": "c58d2449a77887c4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1373,)\n",
      "4995 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  }
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